Quick overview of data analytics, and marketing analytics in retail from practical perspective.

1. DATA ANALYTICS IN RETAIL
By Tanya Zyabkina

2. Analytics in Retail Organization
Marketing
• CRM
• Market Research
• Sales Analytics
• Marketing Mix and
Impact
Measurement
• Testing
• Pricing Strategy
Merchandising
• Planning &
Allocation
• Category Analysis
• Pricing and
Promotions
• Inventory Analysis
Finance
• Product Costs
• Profitability
• Forecasting
Operations
• Labor Scheduling
• Inventory
Optimization
• Logistics

3. Typical Data Structure in Retail
Time Store SKU Units Dollars
Week Region Category
Month Age Model
Quarter Size Color
Year “Same” status Size
Customer ID Date SKU Store Units Dollars
Demos
Sales Data is generally summarized on a store-week level
Customer-level data is used for CRM and segmentation
Important metrics: year over year same store sales
Important metrics: recency, frequency, monetary value (RFM), demographic or behavior segments

4. Building Knowledge with Analytics
Understand the “landscape”, aka deep dives
Identify and track meaningful measures
Develop business insight, i.e. understanding of
how decisions impact the measured outcome
Make better decisions based on the analytical
insights
Ask better questions to be answered next

5. Most Important Thing to Know about
Analytics
The goal of analytics is not to provide you with the data, but be able to
tell you what it means for business decisions.
Example
Question: What percent of our customers repurchase
within 60 days?
Answer: 30%
What is missing in this answer?
How do we know if 30% good or bad? Is it higher or lower during certain
periods of time? Is it higher or lower for certain types of customers? What
about product categories? What can we do to increase that percentage,
and should we?

6. Analytics Dos
• Source errors: outliers, missing data, defects
• Self-inflicted errors: bad formulas, brackets and OR statements, dupes
Data: Validate
• Pull more data than you need for analysis, but always summarize as much as you can
for the output
• Create charts, not tables
Presentation: Less is More
• Comparing to a non-representative set of controls is the most common reason for bad
analytics
• Watch out for results that always seem to go one way
Design: Are you pitting winners against losers?
• Asking “what are you trying to determine from this” often puts you on a shorter path to
a better answer
Wisdom: Ask about the question behind the question

7. Measuring Impact of Marketing Programs
Incremental impact is the change is performance that can be attributed
to the effect of the marketing program.
Can be measured in two ways:
1 Comparison to a matched control group
Marketing Mix Modeling2
It is often hard to determine what happened.
It is much harder to determine what would have happened if we did
not run the program.
The difference between what happened and what would have
happened is incremental impact.

8. Close to Real Life Example
A merchandising manager asks you for help with assessing a Brand X coupon that was
run in Sunday papers in the Northeast market a week ago. She asks you to pull the
number of redemptions.
Coupon redemptions, Brand X 1,000
Brand X Sales Week -3 Week -2 Week -1 Week 0 Incremental Units
Northeast 1,500 1,500 1,500 2,000 500
Brand X Sales Week -3 Week -2 Week -1 Week 0
Northeast, Year Ago 230 270 250 300 200-450
Brand X Sales Week -3 Week -2 Week -1 Week 0
Midwest 1,500 1,500 1,500 1,800 200
Category X Week -3 Week -2 Week -1 Week 0 % Pt Change
Northeast 8,000 8,000 8,000 9,500
Northeast, Year Ago 7,600 7,600 7,600 8,500
YOY Change 5.3% 5.3% 5.3% 11.8% 6.5% 553
Midwest 8,000 8,000 8,000 9,600
Midwest, Year Ago 7,400 7,400 7,400 8,700
YOY Change 8.1% 8.1% 8.1% 10.3% 2.2%
vs. Northeast 4.3% 363

9. Questions to Ask About a CRM Program
• Are your best customers loyal to the brand or to the
discount? If they are loyal to the discount, will they switch
when your competitor provides a better discount?
• How much profit are you giving up to keep your most loyal
customers coming back? Are they more profitable than
other customer groups?
• Is the insight you are getting from the customer database
help us make better decisions? Can we get the same
information some other way?